AI in Schools: Assessment is the Issue

Most of us would likely agree that younger students, in particular, need to master some cognitive skills and that artificial intelligence could impair such learning. In other words, “AI should not do the student's thinking.” 

But recent moves by some schools to ban use of generative AI in primary schools, whether temporary or permanently, are unlikely to stand the test of time, one might suggest. 

Schools arguably have good reasons to restrict AI use to the extent that they try to measure unaided learning. Most teachers are likely to agree that the purpose of any assessment method is to determine what the student knows, not what language models know. 

So cheating and a loss of foundational skills are key issues for schools. Tests and grades can lose their value as assessment tools if teachers cannot separate language model output from student proficiency. 

On the other hand, the fact that educators don't yet know how to redesign education around AI might not be a robust reason for banning AI use. Is that the learner’s problem or the instructor’s problem?

School system / jurisdiction	Policy	What is actually restricted?	Rationale
New York City Public Schools	2026–27 moratorium	Student-facing generative AI banned in grades 2K–8; limited approved use in grades 9–12	Developmental concerns, human interaction, avoiding outsourcing thinking; high schools receive AI literacy
Fairfax County, VA	2026 restrictions	Elementary students prohibited from generative AI; secondary students require specific authorization for specialized uses	"Human-centered" education, caution while formal policy is developed
Seattle Public Schools	Restricted/teacher-directed	AI permitted when teacher authorizes it; unauthorized AI use treated as academic dishonesty	Emphasis on student thinking, transparency and responsible use
Los Angeles Unified	Initially restricted, subsequently opened controlled access	In Dec. 2022 LAUSD temporarily restricted ChatGPT and other GenAI while developing safeguards; subsequently provided access for students 13+	Shift from outright prohibition toward safeguarded use
Fairfax County student devices	Technical blocking	General-purpose GenAI remains blocked on district-issued student devices even though personal-device use is governed by assignment rules	Security and control of school technology
New South Wales, Australia	Assessment restrictions	2026 reforms sharply restrict take-home HSC assessment because of AI concerns	Preserve authenticity of assessed work rather than attempting to ban AI everywhere
England	No blanket student ban	Schools choose their own rules; government recommends supervised, safeguarded student use	AI literacy plus safeguards rather than prohibition

Among the problems is that banning technology does not seem particularly effective, longer term. 

Study	Technology	Finding	Implication for AI bans
Allcott et al., NBER 2026	U.S. school cellphone bans, >43,000 schools	Lockable pouches substantially reduced phone use, but average test-score effects were close to zero; effects on well-being evolved over several years	A ban can change behavior without necessarily producing the hoped-for educational gains
Lichand et al., NBER 2026	Cellphone ban in Rio de Janeiro schools	Phone use fell and test scores increased about 0.06 SD	Restrictions can work when the technology genuinely interferes with learning
Figlio & Özek, NBER 2025	Florida cellphone bans	Short-term suspensions increased; disciplinary effects dissipated after the first year	Enforcement can generate costs of its own
Kessel et al., Sweden	Swedish secondary-school cellphone bans	Found no impact on student performance and could reject even small positive effects	Removing technology doesn't automatically improve learning
Rahali, Kidron & Livingstone, 2024	Rapid review of school smartphone bans	Meta-analysis found a statistically significant but modest overall effect (d=.162), larger for social well-being than academics	Benefits of bans are real but relatively limited
OECD/PISA 2022	School smartphone restrictions	In schools with bans, many students nevertheless reported using phones every day or several times a day	Formal prohibition does not equal behavioral prohibition
EdWeek Research Center, 2024	School cellphone enforcement	Students reported using smartwatches, alternate devices and other methods to circumvent restrictions	Students adapt around technological restrictions
SMART Schools study	UK school phone policies	Restrictive policies reduced phone/social-media use during school, but not overall weekday/weekend use or mental well-being	Restrictions often move behavior rather than eliminate it

Perhaps other bans, such as forbidding student access to smartphones during the school day can work, in a controlled setting, for some types of technology. But it remains far from clear that long-term impact is positive. 

Perhaps bans are good at reducing exposure to a technology inside a controlled environment. They are much less reliable at producing large improvements in educational outcomes. 

Also, “cheating” might not be a technological problem, but a human problem. Students cheated before ChatGPT. Calculators, phones, Google, Wikipedia, friends, answer sites and copied homework provide examples.

AI changes the cost and scale of cheating, but banning one particular tool doesn't necessarily eliminate the underlying incentives. 

The strongest argument for bans, though, is developmental sequencing. We believe students need to learn fundamentals first. That is why art or music students are schooled in classical fundamentals before they start exploring their own interpretations. 

For similar reasons, instructors of mathematics or writing are likely to continue emphasizing mastery of foundational concepts and forms. 

But practical bans will be difficult when AI is built into virtually all major technology platforms people use. 

The issue might be assessment methods more than “learning” methods, though. Probably everyone would agree that students need cognitive mastery of forms before outsourcing work to AI. The relevant issue might be that teachers do not yet have a good way of assessing such mastery when AI tools are available. 

It is one matter to “redesign assessment”methods. But some methods are sort of “brute force,” such as shifting to in-classroom writing rather than “take home” work. 

Redesigning assessment for online learning scenarios will be quite a bit harder, one would think. 

Policy	Short-term effectiveness	Long-term viability
Block ChatGPT on school Wi-Fi/devices	High	Low
Ban AI for particular assignments	High	High
Ban AI for young children	Potentially high	Quite plausible
Ban AI for all K–12 students	Moderate	Low
AI detectors as enforcement	Low–moderate	Very low
Require disclosure of AI use	Moderate	High
Teach AI literacy	Moderate initially	Very high
Redesign assessment around demonstrated competence	High	Very high
Allow AI but require students to explain/defend its output	Moderate–high	Very high

Why So Few Firms Can Point to AI-Driven Productivity Gains

Relatively few firms so far have been able to quantify artificial intelligence productivity gains. But that has been the case for computing in general and the internet: it takes time for innovations to transform business processes. In other words, associated and complementary intangible capital also must be created. 

Studying the productivity impact of computerization on 527 large U.S. firms over 1987-1994, professors Erik Brynjolfsson, MIT Sloan School of Management and Lorin Hitt, University of Pennsylvania, Wharton School found that computerization measured over a five-year to seven-year period found productivity and output contributions up to five times greater than over a one-year period. 

“The results suggest that the observed contribution of computerization is accompanied by relatively large and time-consuming investments in complementary inputs, such as organizational capital,” the researchers say.

We could note the same trend with regards to the internet: productivity did not improve quickly, as whole business processes had to be revised. 

Study	Period / data	What it found	Relevance to the paradox
Brynjolfsson and Hitt, 1996, "Paradox Lost?"	Firm-level IT spending	Found substantial returns to information systems investment at the firm level despite weak aggregate evidence.	Early evidence that the "paradox" could be a measurement or aggregation problem. (PubsOnline)
Brynjolfsson and Hitt, 2000, "Beyond Computation"	Firm-level/case evidence	IT's value depended heavily on organizational transformation and intangible investments.	Probably the most important conceptual explanation for why technology's benefits arrive with a lag. (American Economic Association)
Brynjolfsson and Hitt, 2003, "Computing Productivity"	~600 U.S. firms, 1987–94	Returns to computers were 2–5 times greater over seven years than one year.	Strong evidence that complementary investments take years to generate their full payoff. (ResearchGate)
Oliner and Sichel, 2000	U.S. economy	IT accounted for roughly two-thirds of the acceleration in productivity growth between the first and second halves of the 1990s.	By the late 1990s, the productivity payoff of IT had become visible at the macro level. (Federal Reserve)
Stiroh, 2002	61 U.S. industries	Productivity acceleration was greatest in IT-producing and IT-intensive industries.	Shows diffusion beyond the technology-producing sector. (Federal Reserve Bank of New York)
Barua et al., 2004, "Net-Enabled Business Value"	>1,000 firms	Internet-enabled capabilities improved operational performance and ultimately financial performance; supplier/customer readiness mattered greatly.	Direct evidence that Internet adoption plus complementary organizational capabilities generated business value. (AIS eLibrary)
López Sánchez et al., 2006	464 Spanish firms	Both IT investment and workplace Internet use were associated with higher productivity.	Direct firm-level evidence of an Internet-productivity relationship. (ScienceDirect)
Bloom, Sadun and Van Reenen, 2007/2012	U.S. and European multinationals	U.S. firms obtained substantially greater productivity from IT, largely because of superior management practices.	Powerful evidence that management complements technology. (National Bureau of Economic Research)
Quirós Romero and Rodríguez Rodríguez, 2010	2,168 Spanish manufacturing firms, 2000–05	E-buying significantly improved firm efficiency.	Shows that specific Internet-enabled processes, rather than "Internet adoption" generally, mattered. (ScienceDirect)
Huang and Liu, Taiwan e-commerce study, 2013	Taiwanese manufacturing firms, 1999–2002	E-commerce and R&D both raised productivity; their combination was complementary, with network effects.	Internet value increased when combined with other forms of innovation. (ScienceDirect)
Najarzadeh, Rahimzadeh and Reed, 2014	108 countries, 1995–2010	Internet use had a statistically significant positive relationship with labor productivity.	Evidence that the Internet's productivity effects eventually appeared at the macro level. (ScienceDirect)

Beyond all that, some productivity enhancements are difficult to measure, especially when the capabilities do not have a price tag, and are usable without extra charge, such as search, email, navigation, maps or  social media. 

How do we capture the value of increases in consumer choice, reduced transaction costs, reduced search costs, easier price comparison, better product matching or enhanced convenience?

A corollary might be that the Internet and other general-purpose technologies such as electricity become less visible precisely as their economic importance increases. In other words, the technology becomes embedded in all products and services and becomes less visible as a result. 

So organizational performance enhancements are increasingly difficult to isolate from overall organizational prowess. 

In the case of AI, organizations might already be getting substantial value from AI through:

• employees completing tasks faster

• better-quality work

• broader scope of work

• fewer errors

• faster customer responses

• employees handling more work without additional hiring

• better and faster software development

• faster research and analysis

• improved sales and marketing.

But such improvements are tough to quantify; more qualitative than quantitative in terms of output. 

The upshot is that we should not be surprised when few organizations can point to quantitative output gains using accounting practices. First of all, it is too early for the big results to be proven. Also, some of the immediate gains are difficult to impossible to quantify in output, cash flow or profit figures. 

It will take time, even if investors are impatient.